Systems and methods for detecting and identifying arcing

ABSTRACT

Systems and methods for detecting and identifying arcing are disclosed. A method of detecting arcing includes obtaining data indicative of voltage and data indicative of current, determining a waveform of a cycle of a primary load current according to the data indicative of current, determining at least one noise signal according to the determined waveform of a cycle of the primary load current and the data indicative of current, determining a probability density of the noise signal according to a time window, and comparing the probability density of the noise signal with at least one model probability density.

CROSS-REFERENCE TO RELATED APPLICATION

The present application is a continuation application of U.S. patentapplication Ser. No. 14/206,093, filed on Mar. 12, 2014, in the U.S.Patent and Trademark Office, which claims the right of priority under 35U.S.C. § 119 (e) to U.S. Provisional Application No. 61/781,553, filedon Mar. 14, 2013, in the U.S. Patent and Trademark Office, the entirecontents of which are hereby incorporated by reference.

FIELD OF THE DISCLOSURE

The present application relates to the detection and identification ofarcing, for example, for use with arc fault circuit interrupters.

BACKGROUND OF THE DISCLOSURE

General Description of Arcing in Air and Solid Materials:

Arcing can occur as a result of electrical wire damage. For example, anail or a screw may puncture insulation or create a small break in aconductor. As a result, an arc can form, and traverse air or punchthrough compromised insulation. While all arcs are generally formed insimilar ways, the electrical characteristics of arcing through air canbe different from those of arcing through carbonized insulation.

An arc is an accelerated electron phenomenon. As an electric fieldincreases, for example due to increasing voltage, electrons typicallybegin to move along the electric field, skipping from one atom toanother. In a solid material, an electron flow over a finite amount oftime can be considered a current. This current may be seen as an arc.Yet, when electrons are stripped from atoms at one end of a solidmaterial, a higher electric field strength is typically required tostrip an additional electron. The arc path can as a result becomeunsuitable for sustaining an arc, forcing the arc to find another path.Over time, a used path can eventually recover, though several other arcpaths may be used before a path or a portion of a path regains itssuitability. In air, a similar phenomenon may occur. Yet, the movementof air can create additional features of a discharge. For example,“previous path” may not exist in the context of an arc in air, becauseof the movement of air. Furthermore, even when air is highly confined,it can be heated during arcing, resulting in substantial turbulencewithin the space.

Arcs in a solid material tend to break molecular bonds. They canencourage new bonds and new chemical composition in the solid material.In most plastics, for example, an arc can dissociate carbon fromhydrogen. As hydrogen escapes into air, carbon is left in the plastic,usually with a black appearance, in a process often referred to ascarbonization. Since carbon is more conductive than most plastics, areasof carbonization tend to be locations where arcing often recurs. Theseareas are usually in the form of small black pits, rather than largeareas of carbon, which can nevertheless occur in extreme cases.

Although devices exist for detecting arcing in electrical circuits, theytypically face such problems as oversensitive arcing detection orerroneous arcing identification. For example, conventional arc faultcircuit interrupters often trip when detecting arcing due to the normalfunctioning of electrical components such as electric motors, ratherthan when detecting arcing due to electrical wire damage. Therefore,there is a need for a system that allows for more accurate detection andidentification of potentially unwanted arcing.

SUMMARY OF THE DISCLOSURE

Illustrative embodiments of the present invention address at least theabove problems and/or disadvantages, and provide at least the advantagesdescribed below.

An illustrative method of detecting and identifying arcing can includeobtaining data indicative of voltage and data indicative of current,determining a waveform of a cycle of a primary load current according tothe data indicative of current, determining at least one noise signalaccording to the determined waveform of a cycle of the primary loadcurrent and the data indicative of current, determining a probabilitydensity of the at least one noise signal according to a time window, andcomparing the probability density of the at least one noise signal withat least one model probability density.

An illustrative system for detecting and identifying arcing can includea processor and computer-readable media. The processor can be adapted toobtain and store on the computer-readable media data indicative ofvoltage and data indicative of current. The processor can be furtheradapted to determine a waveform of a cycle of a primary load currentaccording to the data indicative of current. The processor can befurther adapted to determine at least one noise signal according to thedetermined waveform of a cycle of the primary load current and the dataindicative of current. The processor can be further adapted to determinea probability density of the at least one noise signal according to atime window. The processor can be further adapted to compare theprobability density of the at least one noise signal with at least onemodel probability density.

An illustrative system for detecting and identifying arcing can includea current transformer and a resistive load adapted to convert a currentfrom a load to a proportional voltage, an analog-to-digital currentconverter adapted to convert the current to a digitized current at asample frequency, an analog-to-digital voltage converter adapted toconvert the voltage to a digitized voltage at the sample frequency, alearned primary load block adapted to determine a waveform of a cycle ofa primary load current, a subtractor block adapted to determine at leastone noise signal, an arc window comparator adapted to determine a timewindow, a histogram probability density block adapted to determine aprobability density of the at least one noise signal according to thetime window, and a comparator block adapted to compare the probabilitydensity of the at least one noise signal with at least one modelprobability density.

An illustrative system for determining a model probability density cancomprise a current transformer and a resistive load adapted to convert acurrent from a load to a proportional voltage, an analog-to-digitalcurrent converter adapted to convert the current to a digitized currentat a sample frequency, an analog-to-digital voltage converter adapted toconvert the voltage to a digitized voltage at the sample frequency, alearned primary load block adapted to determine a waveform of a cycle ofa primary load current according to the digitized current, a subtractorblock adapted to determine at least one noise signal according to thedetermined waveform of a cycle of the primary load current and thedigitized current, an arc window comparator adapted to determine a timewindow; a histogram probability density block adapted to determine aprobability density of the at least one noise signal according to a timewindow, and computer-readable media adapted to store the probabilitydensity as a model probability density.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other exemplary features, aspects and advantages of thepresent invention will become more apparent from the following detaileddescription of certain exemplary embodiments thereof when taken inconjunction with the accompanying drawings in which:

FIG. 1 illustrates a processing block diagram of a system for detectingand identifying arcing according to an illustrative embodiment of thepresent invention;

FIG. 2 shows an illustrative method of detecting and identifying arcingaccording to an illustrative embodiment of the present invention;

FIG. 3 illustrates a processing block diagram of a system fordetermining a model probability density according to an illustrativeembodiment of the present invention; and

FIG. 4 shows an illustrative method of determining a model probabilitydensity according to an illustrative embodiment of the presentinvention.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

This description is provided to assist with a comprehensiveunderstanding of illustrative embodiments of the present inventiondescribed with reference to the accompanying drawing figures.Accordingly, those of ordinary skill in the art will recognize thatvarious changes and modifications of the illustrative embodimentsdescribed herein can be made without departing from the scope and spiritof the present invention. Also, descriptions of well-known functions andconstructions are omitted for clarity and conciseness. Likewise, certainnaming conventions, labels and terms as used in the context of thepresent disclosure are, as would be understood by skilled artisans,non-limiting and provided only for illustrative purposes to facilitateunderstanding of certain illustrative implementations of the embodimentsof the present invention.

Generally referring to FIGS. 1-4, systems and methods can detect andidentify or assist in the detection and identification of arcing.

Probability Densities Associated with Arcing:

According to an illustrative embodiment of the present invention, aprobability density of a fractal trajectory can be associated witharcing. This probability density can be represented or stored, forexample, in the form of a histogram, or any other form of datarepresentation or storage. A histogram can be indicative of a currentsignal associated with arcing, and can include a plurality of counts.Each count can be associated with an amplitude interval. Each count canbe indicative of a number of amplitude bits from the current signal,which are within the amplitude interval associated with the count.

According to an illustrative embodiment of the present invention, aprobability density can be experimentally obtained for arcing in anymedium, and stored as a model probability density. For example, a modelprobability density for arcing through air, a model probability densityfor arcing through carbonized insulation, or model probability densityfor other potentially unwanted arcing can be obtained. These modelprobability densities can be stored, for example, in computer-readablemedia, including, but not limited to, non-transitory computer-readablemedia.

Matching or comparing a measured probability density to a modelprobability density can help distinguish an arc from random noise ornonlinear loads.

Detecting and Identifying Arcing Using Probability Densities:

In an illustrative embodiment of the present invention, an arc faultcircuit interrupter can be implemented with systems and methods usingmodel probability densities to detect and identify potentially unwantedarcing. A noise signal potentially containing potentially unwantedarcing can be extracted from a measured current. For example, a primaryload current, such as a substantially periodic portion of the current,can be determined and subtracted from the measured current. For example,the primary load current can be learned by a detector and subtractedfrom the measured current. This learning operation can be performed by,for example, finding a rolling average of the current for each intervalwithin a cycle.

Sudden Load Changes:

An illustrative arcing detector can be implemented to compensate forsudden load changes, such as those created by periodically operatingdevices including, for example, conventional electric skillets orthermostatically controlled heaters, and other devices which mayautomatically or manually turn on and off. The arcing detector may needto quickly converge to the load waveform in a case of a device turningon. An error accumulator can be implemented for such quick convergence.

Normal Periodic Arcing:

An illustrative arcing detector can be implemented to ignore generallynormal periodic arcing created by devices such as electric motors. In atypical electric motor, a carbon brush can be pressed against the rotorin the motor. As the rotor spins, the brushes can contact successivecoils in the rotor. The make and break operation of the commutator cancreate normal periodic arcing, which can sometimes be visible throughventilation holes of a device, close to the brushes. These motors areconventionally used in most appliances, such as fans, vacuum cleaners,hair dryers, water pumps, and other such devices. Such normal periodicarcing may be acceptable to the normal functioning of electricalequipment, and thus may not be desired to cause an arc fault circuitinterrupter to trip.

Unlike potentially unwanted arcing such as arcing on a main line, normalperiodic arcing is typically not synchronized to a primary loadfrequency. Although synchronous motors can be synchronized with a mainline, these motors typically produce minimal noise. Generally, a sourceof normal periodic arcing, such as a typical motor, may not besynchronized to the main line, such that normal periodic arcing canoccur at any time during a cycle.

By contrast, potentially unwanted arcing, such as arcing due to adamaged wire, generally occurs only close to a peak voltage portion ofthe current, for example, where an electric field at an arc pointreaches its peak. Consequently, amplitude data at a peak voltage may beused to detect and identify potentially unwanted arcing.

Moreover, the large amount of normal periodic arcing noise typicallycreated by a commutated motor can be detected, and the period of thisnormal periodic arcing noise can be determined. For example, a normalperiodic arcing noise period can be a multiple of a motor speed, and canbe detected or predicted using a periodic noise detector. Usually,normal periodic arcing noise may occur away from the voltage peak of acycle. If normal periodic arcing noise is however predicted to bepresent during the voltage peak, data from this voltage peak may not beincluded in a measured probability density histogram.

Potentially Unwanted Arcing:

According to an illustrative embodiment of the present invention, thepresence of potentially unwanted arcing can be determined by comparing ahistogram indicative of the noise signal at a peak voltage portion ofthe current, or at another time window of the current, over a measuringtime, with a model probability density. Potentially unwanted arcing canbe determined if sufficient energy is present in the histogram, and ifthe histogram adequately matches a model probability density.

Alternatively, a histogram indicative of the noise signal at a peakvoltage portion of the current, or at another time window of thecurrent, over a measuring time, can be stored as a model probabilitydensity. For model probability density, the current can be from aconductor on which arcing is induced through a medium of a desiredcomposition, such as, for example, air or carbon. Moreover, one skilledin the art will appreciate that arcing will likely occur through anymedium in any electrical system after a sufficiently long period oftime, and that such arcing is more likely to occur within a shorterperiod of time in electrical systems with higher voltage. This modelprobability density can be then used with systems or methods fordetecting and identifying arcing according to illustrative embodimentsof the present invention.

Additional Illustrative Embodiments

In illustrative embodiments of the present invention, a measuring devicefor current and a measuring device for voltage may be implemented, suchthat the measured voltage and current are digitized and provided tomultiple processes. Voltage can be used to synchronize a sampling rateto a primary load frequency. Voltage can be low-pass filtered, and theamplitude can be measured. An arc detection can be only initiated duringa desired portion of the voltage waveform, such as, for example, the top10% of the voltage waveform. The current can be sampled, and an averagewaveform for one cycle can be determined. The average current waveformcan be subtracted from the input waveform. Significant deviations fromexpected current outside the peak can be detected. A periodicity of thedeviations can be detected, while searching for normal periodic arcingnoise. A peak containing normal periodic arcing noise can be excludedfrom a histogram of measured probability density. After a number ofcycles or time, the histogram can be compared to model probabilitydensities for arcing in one or more media, and potentially unwantedarcing can be determined if the histogram adequately matches a modelhistogram, and if sufficient energy is present in the histogram. An arcfault circuit interrupter can be tripped if potentially unwanted arcingis determined.

FIG. 1 illustrates a processing block diagram 100 of a system fordetecting and identifying arcing according to an illustrative embodimentof the present invention, which may include current transformer 101,resistive load 102, scale block 103, analog-to-digital converterblock(s) 104, low-pass filter block 105, learned primary load block 106,arc window comparator block 107, phase locked loop block 108, powersupply block 109, bias generator block 110, error accumulator block 111,histogram probability density block 112, normalizing block 113, modelprobability density block 114, comparing block 115, and periodic noisedetector block 116. It will be understood by a person having ordinaryskill in the art that a processor, for example executing instructionsstored on computer-readable media, can perform or simulate functionssubstantially similar to some or all of the functions of elements101-116, for example, using computer-readable media and software.Computer-readable media can include, but is not limited to,non-transitory computer-readable media.

FIG. 2 shows an illustrative method 200 of detecting and identifyingarcing according to an illustrative embodiment of the present invention,which can include obtaining data indicative of voltage and dataindicative of current at step 202, determining a waveform of a cycle ofa primary load current at step 204, determining one or more noisesignals at step 206, determining a probability density of one or morenoise signals according to a time window at step 208, and comparing theprobability density of one or more noise signals with one or more modelprobability densities at step 210.

At step 202, data indicative of voltage and data indicative of current,such as, for example, a digitized voltage and a digitized current, canbe obtained or determined from a load. For example, determining a dataindicative of voltage and data indicative of current can includeobtaining a current and converting the current to a proportional voltageby a current transformer and a resistive load, reducing the voltage toan amplitude and a DC offset suitable for analog-to-digital conversion,converting the current to a digitized current by an analog-to-digitalcurrent converter, and converting the voltage to a digitized voltage byan analog-to-digital voltage converter.

A combination of current transformer 101 and a resistive load 102 can beused to convert current in a hot load to a proportional voltage. Othermethods for measuring current can be used, such as current sensing usinga low ohm resistive element and amplifier, or any other method known inthe art. The current can be obtained, for example by a currenttransformer.

Scale block 103 can reduce a voltage level to an amplitude and DC offsetsuitable for analog-to-digital conversion. A typical input voltage maybe 125 Vrms AC, while most converters may only accept up to 3V. Yet, anyinput voltage may be used. For example, a voltage of about 120 V, 220 V,or 7,500 V can be used to obtain a model probability density for arcing,and/or to detect and identify arcing. The scale block operation canprovide attenuation and can center a signal around a bias voltage.

Analog-to-digital converter blocks 104 can include a voltage converterblock and a current converter block. Illustrative embodiments caninclude one or more analog-to-digital converters. In an illustrativeembodiment of the present invention, a voltage converter may onlyrequire 10 bits of resolution, while a current converter may requiresubstantially better resolution. The resolution of the current may needto be around 1 mA per bit. In a 15 Arms circuit, the maximum peak topeak current may be 42.4 A. A 16 bit analog-to-digital converter mayhave a resolution of 0.65 ma. Increasing the current maximum may requireincreasing the resolution of the current analog-to-digital converter.

Power supply block 109 can be suitable for powering analog and digitalelectronics in a system. Bias generator 110 can be a precision voltagesource set to the center of a range of the analog-to-digital converters.The input analog voltage may be biased such that half ananalog-to-digital converter range is above the bias and half theanalog-to-digital converter range is below the bias.

Low-pass filter block 105 can remove high frequency noise from thevoltage or the data indicative of voltage. In an exemplaryimplementation, low-pass filter block 105 can include a 4 pole IIR 100Hz LONG intermediate precision low-pass filter.

Phase locked loop block 108 can generate a sample clock for theanalog-to-digital converters using the data indicative of voltage. Asample clock with a frequency equal to a multiple of the line frequencymay simplify the implementation of learned primary load block 106. Forexample, a system with a 60 Hz line frequency and a sample rate of48,000 samples per second may have 800 samples for a single sine wave.In this case, the learned primary load current may need a storage sizeof 800 samples to represent one cycle of the waveform. If the samplefrequency and line frequency are not exact multiples, the storagerequirement may include some fraction of a sample. While signalprocessing techniques exist to handle this situation, it may be easierto use a multiple of the line frequency.

Phase locked loop block 108 may lock in on a voltage cycle. A voltagecycle frequency can be determined according to the voltage cycle. Asample period can be determined such that a sample frequency is amultiple of the voltage cycle frequency.

Alternatively, at step 202, a processor can obtain or determine dataindicative of voltage and data indicative of current, which can bestored on computer-readable media including, but not limited to,non-transitory computer-readable media. A processor, for exampleexecuting instructions stored on computer-readable media, can performfunctions substantially similar to some or all of the functions of scaleblock 103, analog-to-digital converter blocks 104, low-pass filter block105 and phase locked loop block 108. For example, a processor can reducea voltage level, convert analog voltage or current to digital voltage orcurrent, remove high frequency noise from the voltage or the dataindicative of voltage, and/or lock in on a voltage cycle to determine asample period.

At step 204, a waveform of a cycle of the current can be determined.Learned primary load block 106 can determine a shape of the primary loadcurrent for one current cycle. The primary load current may not besinusoidal. For example, electronic power supplies, light dimmers, andmany other devices may tend to pull more power during the peak voltage.Learned primary load block 106 can continuously average the waveform ofone or more cycles of the data indicative of current. In an illustrativeembodiment of the present invention, learned primary load block 106 candetermine and/or remember a substantially periodic shape of the primaryload current for a single cycle.

Alternatively, at step 204, a processor can determine a waveform of acycle, which can be stored, for example, on computer-readable mediaincluding, but not limited to, non-transitory computer-readable media. Aprocessor, for example executing instructions stored oncomputer-readable media, can perform perform functions substantiallysimilar to some or all of the functions of learned primary load block106. For example, a processor can continuously average the waveform ofone or more cycles of the data indicative of current and/or store asubstantially periodic shape of the primary load current for a singlecycle in computer-readable media, including, but not limited to,non-transitory computer-readable media.

At step 206, a noise signal can be determined, for example, bysubtracting the determined waveform of a cycle of the primary loadcurrent from a cycle of the data indicative of current. The differencecan be current from potentially unwanted arcs as well as noisy sourcessuch as electric motors. In an illustrative embodiment of the presentinvention, a subtractor block can subtract the determined waveform of acycle of the primary load current from a cycle of the data indicative ofcurrent.

Error accumulator block 111 can determine an error equal to thedifference between the determined waveform of a cycle of the primaryload current and the data indicative of current. The error can thus besubstantially equal to a noise signal. Error accumulator block 111 mayonly permit learned primary load block 106 to make periodic adjustmentsto the learned load at each cycle equal to a fraction of the error. Forexample, error accumulator block 111 may only permit learned primaryload block 106 to add or subtract 10% of the error to the learned loadat each cycle. Error accumulator block 111 may thus help recognizesudden load changes and converge to the load waveform quickly.

Alternatively, at step 206, a processor can determine a noise signal,for example, by subtracting the determined waveform of a cycle of theprimary load current from a cycle of the data indicative of currentwaveform of a cycle. The noise signal can be stored on computer-readablemedia including, but not limited to, non-transitory computer-readablemedia. A processor and/or computer-readable media can perform functionssubstantially similar to some or all of the functions of erroraccumulator block 111. For example, a processor can determine an errorequal to the difference between the determined waveform of a cycle ofthe primary load current and the data indicative of current. The errorcan be substantially equal to a noise signal. A processor may onlypermit periodic adjustments to the learned load at each cycle equal to afraction of the error.

At step 208, a probability density of one or more noise signals can bedetermined according to a time window.

A time window can be determined. For example, arc window comparatorblock 107 can determine a time window as a time interval intended forlocating potentially unwanted arcs. A time window can be close to thepeak voltage or an otherwise upper portion of a voltage waveform. Forexample, a time window can be determined by determining a time intervalassociated with the top 10% of a cycle of the data indicative ofvoltage. Other portions of the voltage waveforms can also be used. Thetime window can be determined according to any desired time windowvalue.

Alternatively, a processor can determine a time window, which can bestored on computer-readable media including, but not limited to,non-transitory computer-readable media. A processor, for exampleexecuting instructions stored on computer-readable media, can performfunctions substantially similar to some or all of the functions of arcwindow comparator block 107.

Determining a probability density can include determining a histogramindicative of one or more noise signals. The histogram can be containedin histogram probability density block 112. The histogram can include aplurality of counts. Each count can be associated with an amplitudeinterval. Each count can be indicative of a number of amplitude bitsboth within the time window of one or more noise signals and within theamplitude interval associated with the count.

For example, the amplitude of the noise during the time window can becompared to a set of evenly spaced fixed values. For example, anillustrative implementation may include 20 comparators equal to 10 mA,20 mA, 30 mA . . . 200 mA. Each time a current amplitude value fallsbetween two comparators, a counter associated with that interval can beincremented. In a simple illustrative implementation, the values can becollected for a fixed period of time up to 5 seconds, or for a fixednumber of cycles. In an illustrative embodiment of the presentinvention, a histogram of, for example, 20 values from the 20 counters,can be forwarded to a comparing operation in comparing block 115.Alternatively, in more sophisticated illustrative embodiments, eachsample may cause one counter to increment while the other 19 countersmay be decreased by a fraction. The result can be essentially 20low-pass filters (with one pole), which can decay exponentially when notincremented. This may eliminate a 5 second delay when collecting thehistogram, and may permit the comparing operation in comparing block tobe performed on each cycle during the time window.

If normal periodic arcing noise is predicted to be present or isotherwise detected or identified during the time window of a noisesignal, a probability density of one or more noise signals may notinclude this noise signal. Such determination can be performed withperiodic noise detector block 116.

Alternatively, at step 208, a processor can determine a probabilitydensity of one or more noise signals according to the time window. Thisprobability density can be stored on computer-readable media including,but not limited to, non-transitory computer-readable media. A processor,for example executing instructions stored on computer-readable media,can perform functions substantially similar to some or all of thefunctions of histogram probability density block 112 and periodic noisedetector block 116. For example, a processor can determine a histogramindicative of one or more noise signals. The histogram can be stored incomputer-readable media including, but not limited to, non-transitorycomputer-readable media. The histogram can include a plurality ofcounts. Each count can be associated with an amplitude interval. Eachcount can be indicative of a number of amplitude bits both within thetime window of one or more noise signals and within the amplitudeinterval associated with the count. A processor can determine, detect,identify or predict normal periodic arcing present during the timewindow of a noise signal. If normal periodic arcing noise is predictedto be present or is otherwise detected, identified or determined duringthe time window of a noise signal, a probability density of one or morenoise signals may not include this noise signal.

At step 210, the probability density of one or more noise signalsaccording to the time window can be compared to one or more modelprobability densities. A model probability density may be a modelprobability density stored in storage media including, but not limitedto, computer-readable media.

Normalizing block 113 can normalize the probability density of one ormore noise signals. For example, a processing block can find the highestenergy or amplitude value in the amplitude intervals. Each amplitudeinterval can be divided by this value. The result can be a probabilitydensity including a maximum value of 1.0. Similarly, model probabilitydensities may already be stored in a normalized form. This process maypermit comparing a shape of the probability density of one or more noisesignals without concern for the actual value of the amplitude. Anotherbyproduct of normalization may be a measurement of energy.

This energy measurement can be performed here or on data input tohistogram probability density block 112. If the energy level is too low,a positive arc detection may not be determined. The intervals in theprobability density of one or more noise signals can contain asufficient amount of energy if they contain more energy than a minimumenergy value. Alternatively, since energy is proportional to the squareof the amplitude, the intervals in the probability density of one ormore noise signals can contain a sufficient amount of energy if thehighest amplitude is greater than a minimum amplitude value.

The normalized probability density of one or more noise signals can thenbe compared to model probability densities. For example, modelprobability density block 114 can be a fixed storage, which can containone or more model probability densities of arc types, including, forexample, a probability density for an arc in air and/or a probabilitydensity for arcing through carbonized insulation.

Comparing block 115 can compare the normalized probability density ofone or more noise signals from normalizing block 113 to the modelprobability densities in model probability density block 114. An exactmatch may not be required. The comparing operation may includingdetermining whether the normalized probability density is similar to atleast one of the model probability densities in model probabilitydensity block 114 according to a pattern matching algorithm. If asufficiently close match is found and the probability density of one ormore noise signals contains sufficient energy, the arc may be detectedand identified, and may be indicated as a positive arc detection at theoutput, which can cause an arc fault circuit interrupter to trip. Acircuit may remain open until it is closed or until the device is reset,manually or automatically.

Whether the normalized probability density is similar to at least one ofmodel probability densities can be determined by pattern matching. Forexample, if the counts associated with a histogram of the normalizedprobability density are within a range from the corresponding countsassociated with a histogram of a model probability density, similaritycan be determined. Alternatively, if the constant coefficients of apolynomial approximation of the histogram of the normalized probabilitydensity are within a range from the corresponding constant coefficientsof a polynomial approximation of the counts associated with a histogramof a model probability density, similarity can be determined.Alternatively, any other pattern matching methods or algorithms known inthe art can be used. Some algorithms may include shifting data to allowfor easier comparison of the normalized probability density to a modelprobability density.

Alternatively, at step 210, a processor can compare the probabilitydensity of one or more noise signals according to the time window to oneor more model probability densities. A model probability density may bestored in computer-readable media including, but not limited to,non-transitory computer-readable media. A processor, for exampleexecuting instructions stored on computer-readable media, can performfunctions substantially similar to some or all of the functions ofhistogram probability density block 112, normalizing block 113, modelprobability density block 114 and comparing block 115. For example, aprocessor can normalize the probability density of one or more noisesignals. A processor can find the highest energy or amplitude value inthe amplitude intervals and divide each amplitude interval by thisvalue. A processor can compare the normalized probability density of oneor more noise signals to model probability densities. A processor candetermine if the probability of one or more noise signals containssufficient energy using a minimum energy value stored incomputer-readable media including, but not limited to, non-transitorycomputer-readable media. If a sufficiently close match is found and theprobability density of one or more noise signals contains sufficientenergy, the arc may be detected and identified, and indicated as apositive arc detection at the output, which can cause an arc faultcircuit interrupter to trip. A circuit may remain open until it isclosed or until the device is reset, manually or automatically. Themethods and system disclosed herein in accordance with illustrativeembodiments of the present invention can be particularly useful inapplications where an arc of relatively small size exists over aconsiderable period of time. For example, it can be useful to employ amethod or system in accordance with illustrative embodiments of thepresent invention to detect an arc in the electrical wiring system of anairplane or cruise ship or other location where people are generally notpresent during a testing period and therefore having to comply with ULstandard arc detection time constraints.

FIG. 3 illustrates a processing block diagram 300 of a system fordetermining a model probability density according to an illustrativeembodiment of the present invention, which may include currenttransformer 301, resistive load 302, scale block 303, analog-to-digitalconverter block(s) 304, low-pass filter block 305, learned primary loadblock 306, arc window comparator block 307, phase locked loop block 308,power supply block 109, bias generator block 110, error accumulatorblock 311, histogram probability density block 312, normalizing block313, model probability density block 314, and periodic noise detectorblock 316. Elements 301-313 and 316 can perform functions substantiallysimilar to those performed by elements 101-113 and 116 in illustrativeprocessing block diagram 100.

FIG. 4 shows an illustrative method 400 of determining a modelprobability density according to an illustrative embodiment of thepresent invention, which can include obtaining data indicative ofvoltage and data indicative of current at step 402, determining awaveform of a cycle of a primary load current at step 404, determiningone or more noise signals at step 406, determining a probability densityof one or more noise signals according to a time window at step 408, anddetermining a model probability density at step 410. The current can befrom a conductor on which arcing is induced through a medium of adesired composition, such as, for example, air or carbon. Moreover, oneskilled in the art will appreciate that arcing will likely occur throughany medium in any electrical system after a sufficiently long period oftime, and that such arcing is more likely to occur within a shorterperiod of time in electrical systems with higher voltage. Accordingly,exemplary embodiments of the present invention facilitate determinationof model arc probability density at different voltage magnitudes wherevoltages of about 120 V, 220 V, or 7,500 V can be used to obtain a modelprobability density for arcing. High voltage application may furtherbenefit from the modeling and detection techniques of the presentinvention, since the time period during which an arcing event can occuris shorter. Steps 402-408 can be substantially similar to steps 202-208in illustrative method 200.

At step 410, a model probability density can be determined. For example,a model probability density can be substantially equal to a probabilitydensity or normalized probability density of one or more noise signalsof step 408. In an illustrative embodiment of the present invention, amodel probability density can be stored in model probability densityblock 314. In an illustrative embodiment of the present invention, amodel probability density can be stored in computer-readable mediaincluding, but not limited to, non-transitory computer-readable media.

Normalizing block 313 can normalize the probability density of one ormore noise signals. For example, a processing block can find the highestenergy or amplitude value in the amplitude intervals. Each amplitudeinterval can be divided by this value. The result can be a probabilitydensity including a maximum value of 1.0. Similarly, model probabilitydensities may already be stored in a normalized form. This process maypermit comparing a shape of the probability density of one or more noisesignals without concern for the actual value of the amplitude. Anotherbyproduct of normalization may be a measurement of energy.

This energy measurement can be performed here or on data input tohistogram probability density block 312. If the energy level is too low,a model probability density may not be determined. The intervals in theprobability density of one or more noise signals can contain asufficient amount of energy if they contain more energy than a minimumenergy value. Alternatively, since energy is proportional to the squareof the amplitude, the intervals in the probability density of one ormore noise signals can contain a sufficient amount of energy if thehighest amplitude is greater than a minimum amplitude value. Thenormalized probability density of one or more noise signals can be thenstored in a model probability density block 314. This model probabilitydensity can be then used with systems or methods for detecting andidentifying arcing according to illustrative embodiments of the presentinvention.

The components of the illustrative devices, systems and methods employedin accordance with the illustrated embodiments of the present inventioncan be implemented, at least in part, in digital electronic circuitry,analog electronic circuitry, or in computer hardware, firmware,software, or in combinations of them. These components can beimplemented, for example, as a computer program product such as acomputer program, program code or computer instructions tangiblyembodied in an information carrier, or in a machine-readable storagedevice, for execution by, or to control the operation of, dataprocessing apparatus such as a programmable processor, a computer, ormultiple computers. Examples of the computer-readable recording mediuminclude, but are not limited to, read-only memory (ROM), random-accessmemory (RAM), CD-ROMs, magnetic tapes, floppy disks, optical datastorage devices. It is envisioned that aspects of the present inventioncan be embodied as carrier waves (such as data transmission through theInternet via wired or wireless transmission paths). A computer programcan be written in any form of programming language, including compiledor interpreted languages, and it can be deployed in any form, includingas a stand-alone program or as a module, component, subroutine, or otherunit suitable for use in a computing environment. A computer program canbe deployed to be executed on one computer or on multiple computers atone site or distributed across multiple sites and interconnected by acommunication network. The computer-readable recording medium can alsobe distributed over network-coupled computer systems so that thecomputer-readable code is stored and executed in a distributed fashion.Also, functional programs, codes, and code segments for accomplishingthe present invention can be easily construed as within the scope of theinvention by programmers skilled in the art to which the presentinvention pertains. Method steps associated with the illustrativeembodiments of the present invention can be performed by one or moreprogrammable processors executing a computer program, code orinstructions to perform functions (for example, by operating on inputdata and/or generating an output). Method steps can also be performedby, and apparatus of the invention can be implemented as, specialpurpose logic circuitry, for example, an FPGA (field programmable gatearray) or an ASIC (application-specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The essential elements of a computer area processor for executing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, forexample, magnetic, magneto-optical disks, or optical disks. Informationcarriers suitable for embodying computer program instructions and datainclude all forms of non-volatile memory, including by way of example,semiconductor memory devices, for example, EPROM, EEPROM, and flashmemory devices; magnetic disks, for example, internal hard disks orremovable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.The processor and the memory can be supplemented by, or incorporated inspecial purpose logic circuitry.

The above-presented description and figures are intended by way ofexample only and are not intended to limit the present invention in anyway except as set forth in the following claims. It is particularlynoted that persons skilled in the art can readily combine the varioustechnical aspects of the various elements of the various exemplaryembodiments that have been described above in numerous other ways, allof which are considered to be within the scope of the invention.

The above-described exemplary embodiments of an apparatus, system andmethod in computer-readable media include program instructions toimplement various operations embodied by a computer. The media may alsoinclude, alone or in combination with the program instructions, datafiles, data structures, and the like. The media and program instructionsmay be those specially designed and constructed for the purposes of thepresent invention, or they may be of the kind well-known and availableto those having skill in the computer software arts. Examples ofcomputer-readable media include magnetic media such as hard disks,floppy disks, and magnetic tape; optical media such as CD ROM disks andDVD; magneto-optical media such as optical disks; and hardware devicesthat are specially configured to store and perform program instructions,such as read-only memory (ROM), random access memory (RAM), flashmemory, and the like. The media may also be a transmission medium suchas optical or metallic lines, wave guides, and so on, and is envisionedinclude a carrier wave transmitting signals specifying the programinstructions, data structures, and so on. The computer-readablerecording medium can also be distributed over network-coupled computersystems so that the computer-readable code is stored and executed in adistributed fashion. Examples of program instructions include bothmachine code, such as produced by a compiler, and files containinghigher level code that may be executed by the computer using aninterpreter. The described hardware devices may be configured to act asone or more software modules in order to perform the operations of theabove-described embodiments of the present invention.

Although exemplary embodiments of the present invention have beendisclosed for illustrative purposes, those skilled in the art willappreciate that various modifications, additions, and substitutions arepossible, without departing from the scope of the present invention.Therefore, the present invention is not limited to the above-describedembodiments, but is defined by the following claims, along with theirfull scope of equivalents.

What is claimed is:
 1. A method for detecting and identifying arcingcomprising: converting a current from a load to a proportional voltage;converting the current to a digitized current at a sample frequency;converting the proportional voltage to a digitized voltage at the samplefrequency; determining a waveform of a cycle of a primary load currentbased on the digitized current; and determining at least one noisesignal according to the determined waveform of the cycle of the primaryload current and the digitized current; setting a time window within acycle of the digitized voltage by setting a start time and a stop timeof a time interval based on voltage amplitude at the start time and thestop time being within a predetermined value of the peak voltage of thewaveform of the cycle of the digitized voltage; determining aprobability density of the at least one noise signal according to theset time window; comparing the probability density of the at least onenoise signal with at least one model probability density; and generatingan output indicative of a positive arc detection based on the comparingwhen the time window is set based on values of the digitized voltageindicative of the arc.
 2. The method for detecting and identifyingarcing of claim 1, wherein: the converting of the current from the loadto the proportional voltage comprises reducing the voltage to anamplitude and a DC offset suitable for analog-to-digital conversionprior to the converting of the current to the digitized current and theconverting of the voltage to the digitized voltage.
 3. The method fordetecting and identifying arcing of claim 2, further comprising removinghigh frequency noise from the voltage.
 4. The method for detecting andidentifying arcing of claim 2, wherein the sample frequency is equal toa multiple of the voltage cycle frequency.
 5. The method for detectingand identifying arcing of claim 4, further comprising determining thevoltage cycle frequency.
 6. The method for detecting and identifyingarcing of claim 1, further comprising determining one of the at leastone noise signal by subtracting the determined waveform of a cycle ofthe primary load current from one of at least one cycle of the digitizedcurrent.
 7. The method for detecting and identifying arcing of claim 6,further comprising adjusting the determined waveform of at least aportion of a cycle of the primary load current according to one of theat least one noise signals.
 8. The method for detecting and identifyingarcing of claim 7, wherein the adjusting of the determined waveform isby an error accumulator block.
 9. The method for detecting andidentifying arcing of claim 7, wherein the adjusting of the determinedwaveform comprises adding or subtracting a fraction of the one of the atleast one noise signal to or from the determined waveform of a cycle ofthe primary load current.
 10. The method for detecting and identifyingarcing of claim 1, wherein the voltage amplitude at the start of thetime interval and the stop of the time interval is within an upperportion of the peak voltage of the cycle of the digitized voltage. 11.The method for detecting and identifying arcing of claim 1, wherein thedetermining of the probability density of the at least one noise signalcomprises determining a histogram indicative of the at least one noisesignal, wherein the histogram comprises a plurality of counts, whereineach count is indicative of a number of amplitude bits of the at leastone signal within one of a plurality of amplitude intervals, and whereinthe amplitude bits are within the time window of one of the at least onenoise signal.
 12. The method for detecting and identifying arcing ofclaim 11, further comprising comparing the probability density of the atleast one noise signal with at least one model probability density by atleast one of: determining a normalized histogram indicative of the atleast one noise signal by normalizing the histogram indicative of the atleast one noise signal; and determining a positive arc detection if thenormalized histogram indicative of the at least one noise signal issimilar to one of the at least one model probability density accordingto a pattern matching algorithm.
 13. The method for detecting andidentifying arcing of claim 12, further comprising normalizing thehistogram indicative of the at least one noise signal by dividing eachamplitude interval by a highest amplitude value in the plurality ofamplitude intervals.
 14. The method for detecting and identifying arcingof claim 12, further comprising determining the positive arc detectiononly if the histogram indicative of a highest amplitude value in aplurality of intervals in the histogram indicative of the at least onenoise signal is greater than a minimum amplitude value.
 15. The methodfor detecting and identifying arcing of claim 12, wherein the at leastone model probability density is a normalized model probability density.16. The method for detecting and identifying arcing of claim 1, wherein,if a normal periodic arcing noise is determined in one of the at leastone noise signals, the probability density is determined by excludingthe one of the at least one noise signal.
 17. The method for detectingand identifying arcing of claim 1, wherein the at least one modelprobability density comprises at least one arc probability density. 18.The method for detecting and identifying arcing of claim 1, furthercomprising outputting an arc detect signal for tripping an arc faultcircuit interrupter upon the positive arc detection.
 19. A method fordetermining a model probability density for detecting and identifyingarcing comprising: measuring a current in a conductor when inducingarcing in the conductor; converting the current to a proportionalvoltage; converting the current to a digitized current at a samplefrequency; converting the proportional voltage to a digitized voltage atthe sample frequency; determining a waveform of a cycle of a primaryload current according to the digitized current; determining at leastone noise signal according to the determined waveform of the cycle ofthe primary load current and the digitized current; setting a timewindow within a cycle of the digitized voltage by setting a start timeand a stop time of a time interval based on voltage amplitude at thestart time and the stop time being within a predetermined value of thepeak voltage of the waveform of the cycle of the digitized voltage;determining a probability density of the at least one noise signalaccording to the time window; determining a model probability density ata voltage magnitude; and storing data indicative of the modelprobability density at the voltage magnitude in a non-transitorycomputer-readable medium as a reference for a positive arc detection atthe voltage magnitude.
 20. The method for detecting and identifyingarcing of claim 1, wherein the voltage amplitude at the start of thetime interval and the stop of the time interval is within 10% of thepeak voltage of the cycle of the digitized voltage.